Discovering Chatbot's Self-Disclosure's Impact on User Trust, Affinity, and Recommendation Effectiveness

• URL: http://arxiv.org/abs/2106.01666v1
• Date: Thu, 3 Jun 2021 08:16:25 GMT
• Title: Discovering Chatbot's Self-Disclosure's Impact on User Trust, Affinity, and Recommendation Effectiveness
• Authors: Kai-Hui Liang, Weiyan Shi, Yoojung Oh, Jingwen Zhang, Zhou Yu
• Abstract summary: We designed a social bot with three self-disclosure levels that conducted small talks and provided relevant recommendations to people. 372 MTurk participants were randomized to one of the four groups with different self-disclosure levels to converse with the bot on two topics, movies and COVID-19.
• Score: 39.240553429989674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, chatbots have been empowered to engage in social conversations with humans and have the potential to elicit people to disclose their personal experiences, opinions, and emotions. However, how and to what extent people respond to chabots' self-disclosure remain less known. In this work, we designed a social chatbot with three self-disclosure levels that conducted small talks and provided relevant recommendations to people. 372 MTurk participants were randomized to one of the four groups with different self-disclosure levels to converse with the chatbot on two topics, movies, and COVID-19. We found that people's self-disclosure level was strongly reciprocal to a chatbot's self-disclosure level. Chatbots' self-disclosure also positively impacted engagement and users' perception of the bot and led to a more effective recommendation such that participants enjoyed and agreed more with the recommendations.

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